Delirium: non-pharmacological and pharmacological management
Bibliographic record
Abstract
Delirium is an acute onset, fluctuant, confusional state with cognitive, emotional, perceptual, psychomotor and sleep–wake cycle disturbances. It is often worse in the evening and at night, particularly with underlying dementia.1 Delirium is often not diagnosed due to fluctuating signs and symptoms. The most common clinical subtype in palliative care is hypoactive delirium, with reduced psychomotor activity.2 Delirium is especially common in palliative care, almost ubiquitous towards the end of life; up to 88% of patients develop delirium in the last weeks to hours of life.3 Older age and dementia are major risk factors. Current and projected demographic changes, with an increased elderly population, signal a need for physicians to have a better awareness of delirium diagnosis and assessment. A high level of suspicion and multidisciplinary team involvement is needed in diagnosis and management. The primary management is rapid diagnosis, as mortality increases with delay. This includes history (importantly collateral histories from carers, family and staff), examination and appropriate investigation (according to goals of care). The aim is to make the diagnosis and if possible confirm the cause(s). It is also important to determine the impact on the patient and family/carer and ascertain their needs. Delirium screening and diagnostic tools can help improve diagnosis but seldom used.4 Clinicians should use low burden validated screening tools including the Single Question in Delirium, Nursing Delirium Screening Scale, Delirium Observation Screening Scale and the Confusion Assessment Method (CAM).2 The CAM needs proper training. The 4AT assesses cognition (specifically attention) and is popular in elderly medicine. Currently, it has not been formally validated in palliative care patients but has been used in a hospice setting.5 Diagnostic criteria appear in the …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".